Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control

Wei Yen Wang*, Guan Ming Chen, C. W. Tao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)


In this paper, an output-feedback direct adaptive fuzzy neural controller for an anti-lock braking system (ABS) is developed. It is assumed that only the system output, the wheel slip ratio, is available for measurement. The main control strategy is to force the wheel slip ratio tracking variant optimal slip ratios, which may vary with the environment and assumed to be known during the vehicle-stopping period. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated.

Original languageEnglish
Pages (from-to)3675-3680
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2003
Externally publishedYes
EventSystem Security and Assurance - Washington, DC, United States
Duration: 2003 Oct 52003 Oct 8


  • Anti-lock brake system
  • Fuzzy neural control
  • Tracking optimal slip ratios

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


Dive into the research topics of 'Stable anti-lock braking system using output-feedback direct adaptive fuzzy neural control'. Together they form a unique fingerprint.

Cite this